In modern Data Engineering, there is an unspoken fetish for "Real-Time." If you ask any business stakeholder how fast they need their dashboard to update, the default answer will always be: "As fast as possible."
This drives well-intentioned engineers to design incredibly complex architectures. We spin up Kafka clusters, implement Flink, and wrestle with latency, late-arriving data, and tumbling windows. All to have data flowing in milliseconds. But the harsh reality is that the vast majority of companies are building Ferraris just to sit in rush-hour traffic.
1. The Actionability Gap (The Golden Question)
The biggest mistake when choosing a streaming architecture isn't technical; it's a business mistake. Before implementing real-time pipelines, the only question that matters is:
"Does the company have the operational capacity to make a decision in milliseconds?"
